Testing for fitness epistasis in a transplant experiment identifies a candidate adaptive locus in Timema stick insects.

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genetic heterogeneity and epistasis, would be expected to increase in severity with the geographical range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (less than 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than the previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Expansion of experimental genetics approaches for Plasmodium berghei with versatile transfection vectors

Natural variation for an adaptively important life history trait is largely due to variation at a single, major-effect locus with multiple alleles, demonstrating that not all complex traits are massively polygenic.

With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history.This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Plasma triglyceride (TG) concentration is reemerging as an important cardiovascular disease risk factor. More complete understanding of the genes and variants that modulate plasma TG should enable development of markers for risk prediction, diagnosis, prognosis, and response to therapies and might help specify new directions for therapeutic interventions. Recent genome-wide association studies (GWAS) have identified both known and novel loci associated with plasma TG concentration. However, genetic variation at these loci explains only ∼10% of overall TG variation within the population. As the GWAS approach may be reaching its limit for discovering genetic determinants of TG, alternative genetic strategies, such as rare variant sequencing studies and evaluation of animal models, may provide complementary information to flesh out knowledge of clinically and biologically important pathways in TG metabolism. Herein, we review genes recently implicated in TG metabolism and describe how some of these genes likely modulate plasma TG concentration. We also discuss lessons regarding plasma TG metabolism learned from various genomic and genetic experimental approaches. Treatment of patients with moderate to severe hypertriglyceridemia with existing therapies is often challenging; thus, gene products and pathways found in recent genetic research studies provide hope for development of more effective clinical strategies.

BackgroundWe consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. arXiv:1710.01788, 2017; Yu et al in Front Big Data 2:27, 2019), but these ignore population structure and familial relatedness (Yu et al in Nat Genet 38:203–208, 2006).ResultsThis work addresses this gap by proposing a novel class of regularized multi-trait linear mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations.ConclusionsThe proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

Sugar kelp (Saccharina latissima) is an ecologically and increasingly economically important kelp, distributed from temperate to Arctic rocky shores. However, S. latissima is presently threatened by ongoing climate changes. Genetic variations have previously been identified across S. latissima populations. However, little is known regarding the genetic basis for adaptation and acclimation to different environmental conditions. In this study, a common garden experiment was performed with sporophytes originated from North-Norway (NN), Mid-Norway (MN), and South-Norway (SN), representing areas with highly different temperatures and photoperiods. Transcriptomic analyses revealed significant variation in the gene expression of cultures from North-Norway, associated with low temperature and long photoperiods, compared to Mid- and South-Norway. Differentially expressed genes included genes linked to photosynthesis, chlorophyll biosynthesis, and heat response, suggesting that they are directly involved in temperature and light adaptation. In addition, genes related to growth, metabolism, protein synthesis, and translation were upregulated in the NN genotype, providing evidence that the NN genotype is better adapted to low temperatures than the SN and MN genotypes. Significant variation in gene expression among populations found in this study is influenced by the environment, but genetic differentiation by origin seems to play a role as responses were population specific. This study provides a baseline for deeper insight into the local adaptation potential of S. latissima populations along the Norwegian Coast with implications for the conservation of natural populations.

Johanna Schmitt It is a great pleasure to help honour the 2014 recipient of the Molecular Ecology Prize: Johanna Schmitt, Professor of Evolution and Ecology and of Population Biology at the University of California, Davis. Johanna, or Annie as she is known by friends and colleagues, has had tremendous influence on the field of ecological genetics throughout her career, and her recent work on the genetic basis of adaptation in Arabidopsis thaliana is some of the most ambitious applications of genomic methods to test hypotheses of ecological and evolutionary dynamics. Entering the field of evolutionary genetics and genomics from the field of ecology, she has infused genetic studies of adaptation with a rich and nuanced view of the ecological environment as seen from the perspective of her study organisms. Anyone who has walked in the woods with her will recognize her plants' eye view in her research. As her former postdoc John Stinchcombe observed, ‘one of the things I find remarkable about Annie (among many) is that as the field has transitioned from a few genes or anonymous markers to whole genome level variation, she's never lost her “feel for the organism” or sight of the larger ecological or evolutionary questions that motivated her to go down this path’. Annie majored in Biology at Swarthmore College and continued her PhD in Biology at Stanford University, with Ward Watt as her advisor. There, she wrote her dissertation on the pollination biology of Scenecio and Linanthus, cultivating interests in the population genetic consequences of density-dependent pollination dynamics (e.g. Schmitt 1983a,b). It was during her postdoctoral work at Duke University, with Janis Antonovics (whom she admired as a great female role model, until she met him in person), that she developed her signature methodology of applying genetic designs to clever and complex field experiments. This approach had two important consequences for her own research and for the field of ecological genetics: first, it illustrated how ecological manipulations can be combined with genetic analysis to test evolutionary hypotheses. For example, her work at Duke tested how genetic diversity within local neighbourhoods can influence competitive interactions and adverse effects of herbivores, relating these dynamics to the evolution of sexual reproduction (Schmitt & Antonovics 1986b; Schmitt & Ehrhardt 1987; Kelley et al. 1988). The focus on sexual reproduction also motivated her to distinguish maternal vs. paternal effects on progeny phenotypes, bringing into focus the phenomenon of maternal effects or cross-generational phenotypic plasticity (Antonovics & Schmitt 1986; Schmitt & Antonovics 1986a). Second, this approach illustrated the strong environmental context of the expression of genetically based traits. Her subsequent work, which she continued at her first faculty appointment at Brown University, engaged the evolutionary and ecological consequences of this environment-dependent genetic expression or genotype–environment interaction. She made phenotypic plasticity a central focus of her research programme (Schmitt et al. 1992; Schmitt 1993, 1995). It was this work that pioneered methods for testing the adaptive significance of phenotypic plasticity, both within and across generations (Schmitt 1993, 1997; Wulff et al. 1994, Schmitt et al. 1999). Her combination of environmental manipulations, phenotypic and genetic manipulations, and measurements of environment-dependent natural selection became the gold standard of tests for adaptive plasticity. Her work on shade avoidance responses in Impatiens capensis unambiguously demonstrated adaptive plasticity and documented that not only did phenotypes change in response to environmental conditions, but genetic variances and covariances did as well (Dudley & Schmitt 1996; Schmitt & Dudley 1996; Donohue et al. 2000a,b). That is, the genetic basis of traits under selection, and the genetic relationships among them, depended strongly on the ecological environment they experienced. Annie's work on shade avoidance responses engaged not only the quantitative genetic basis of this complex trait, but the molecular genetic pathways associated with it as well. Shade avoidance—the ability of plants to elongate in response to vegetation shade—was long known to be mediated by the plant photoreceptors, phytochromes (Schmitt & Wulff 1993). During a sabbatical at the University of Leicester, she collaborated with Alex McCormac and Harry Smith to test how the genetic disruption of phytochrome function would alter shade avoidance and fitness. Using transgenic lines of tobacco whose shade avoidance ability had been blocked, and constitutively shade-avoiding mutants of Brassica, they demonstrated a significant fitness disadvantage of inappropriate shade avoidance responses (Schmitt et al. 1995). This was her first work that employed tools of molecular genetics to test ecological hypotheses. While continuing to investigate the quantitative genetic basis of diverse plastic responses to vegetation shade, Annie began to explore other genetic methods to evaluate their genetic architecture. Like other evolutionary geneticists at the time, she discovered the utility of employing natural genetic variation in ecologically important traits to investigate their genetic basis through quantitative trait locus (QTL) mapping. At the time when QTL analysis was just beginning to be broadly applied to identify loci associated with ecologically significant phenotypes, Annie and her associates implemented a highly ambitious QTL study using Arabidopsis thaliana under field conditions to map not only well-defined phenological and morphological phenotypes, but fitness itself. This intense collaborative effort, initially supported by an NSF FIBR grant, was among the very first to map loci associated with fitness under natural conditions in contrasting geographical sites (Weinig et al. 2002, 2003a,b,c). By demonstrating that some genetic loci were associated with fitness only in one location but neutral in another, while other genetic loci were associated with fitness in both locations, but in opposite directions, this study illustrated how QTL analysis could be employed to resolve long-standing issues of trade-offs in adaptation across geographical locations—specifically revealing instances of conditional neutrality and evidence of antagonistic pleiotropy. The success of this research programme spawned a monster, according to the numerous participants of the next major research effort. Encouraged by the success of the two-site field study with numerous recombinant inbred lines, the team, with some new recruits, initiated a study using four sites across the native range of A. thaliana, from Oulu, Finland to Valencia, Spain, in which hundreds of natural ecotypes combined with a strategic array of mutants, were planted for continuous monitoring. Simultaneously with this ambitious field experiment, creative modelling efforts were being developed to predict the flowering time of specific genotypes under diverse climatic scenarios using agronomic models. This synthesis of genetics, ecology, agronomy and mathematical modelling was unique, and it provided unique insight into the genetic basis of adaptation. Their synthetic approach revealed, for example, that even well-known flowering time genes are expected to exhibit (and did exhibit) effects on flowering time only under certain ecological circumstances and life history backgrounds (Wilczek et al. 2009; Chew et al. 2012). The feat of bringing evolutionary ecologists (Cynthia Weinig, Tonia Korves, Amity Wilczek) in dialogue with population geneticists (Michael Purugganan), molecular geneticists (George Coupland, Rick Amasino, Caroline Dean) and modellers (Steve Welch), while engaging international collaborators (Outi Savolainen, Matthias Hoffmann) in fieldwork, was a Herculean accomplishment. A series of articles from this work was published in Science, PNAS, Molecular Ecology and a number of other prominent journals. Among the most notable findings of this programme were that A. thaliana shows evidence of climate adaptation, with geographic clines in adaptively significant life history traits as well as the loci associated with those traits (Caicedo et al. 2004; Stinchcombe et al. 2004, 2005; Korves et al. 2007; Wilczek et al. 2010). Moreover, genome-wide association studies revealed associations of loci with climate factors across the genome (Fournier-Level et al. 2011, 2013). Most recently (Wilczek et al. 2014), the team found evidence that climate change has caused banked seeds to no longer be optimally adapted to their locations of collection, but that that ecotypes from historically warmer locations performed better under current (warmer) conditions than banked seeds in their native location. As such, immigration of more warm-adapted genotypes into areas with climate change, not emergence from the seed bank or introduction of local banked seeds, is expected to be more effective at maintaining populations in the face of climate change. These empirical data, combined with predictive modelling, establishes a new standard for predictions of how organisms can respond to climate change. Annie was involved at the beginning stages of developing model genetic organisms into model ecological organisms. She helped shape the sorts of questions that could be addressed with this sort of collaboration and made ecological genetics a collaborative endeavour between ecologists, population geneticists and molecular geneticists. Always promoting collaboration over competition, she brokered many matches between PIs studying related phenomena and proposed opportunities to combine efforts in synergistic directions. The field has her to thank for the open and collaborative spirit she has infused it with. In addition to shaping the collaborative nature of the field of ecological genetics, Annie has been a valuable mentor to people at all stages. At Brown, she worked closely with her undergraduate students to involve them with every step of their research projects, from helping to design experiments to data collection, and analysis and presentation. Many of us are grateful for this effort, which has produced so many excellent students who have joined our laboratories as graduate students or technicians. It was here, too, that so many of her postdocs learned the craft of designing undergraduate projects that were self-contained, challenging and rewarding, providing a model for tapping the unique resources of undergraduates in research. Her numerous postdocs also benefitted from being members of such a cohesive laboratory, in which laboratory members could count on each other for technical help and conceptual exchange. Annie's generosity of time, creativity and opportunity were critical to the professional development of many of us. Personally, I will never take for granted the extreme generosity she extended to me when, after a very ill-timed postdoc in Yemen during what turned into its civil war, I found myself evacuated back stateside with no backup plan. I basically knocked on her door to ask for a short-term landing pad, and she opened it up in a manner I could never have expected. At that critical, awkward and very tricky time in my career, she welcomed me into her laboratory, involved me in the ongoing research and gave me new skills, intellectual companionship and a model for how to run a laboratory that was collegial, engaging and effective. I am certain that anyone who spent time in her laboratory benefitted in the same way, and several of her former postdocs (including Susan Dudley, Massimo Pigliucci, Cynthia Weinig, John Stinchcombe, Amity Wilczek and others) have expressed the same appreciation over the years. Annie has amassed several honours as a result of her creative contributions, including election to the National Academy of Sciences, the American Academy of Arts and Sciences, the American Association for the Advancement of Sciences and an Alexander von Humboldt Award, among others. She has been the President of the major professional societies in her field: the Society for the Study of Evolution and the American Society of Naturalists. While at Brown University, she was Stephen T. Olney Professor of Natural History, and she was also the director of the Environmental Change Initiative there, where she exercised her remarkable ability to communicate and synthesize across scientific subfields. UC Davis is now the beneficiary of Annie's energy and expertise, after she moved there in 2012. This Molecular Ecology Prize serves to honour her past accomplishments and inspire curiosity for what is to come.

Three Genome-wide Association Studies and a Linkage Analysis Identify HERC2 as a Human Iris Color Gene

Guidelines for Genome-Wide Association Studies

Three Genome-wide Association Studies and a Linkage Analysis Identify HERC2 as a Human Iris Color Gene

Complex trait genetics: mapping, correlation and causation

The aim of Genome Wide Association Studies (GWAS) is to identify markers in tight linkage disequilibrium with loci controlling quantitative trait variation. These markers can then be used in marker-assisted selection (MAS) in fruit crops such as apple. The GWAS approach involves both phenotyping of a large population of mostly unrelated individuals for the traits of interest, and genotyping at high marker density. In the EU-FP7 project FruitBreedomics, almost 1,200 European diploid dessert apple accessions (old and/or local cultivars) from six germplasm collections were genotyped with the Affymetrix Axiom_Apple480K array (487,000 SNPs). Phenotypic data on a large number of traits have been gathered during the project. Here we focus on flowering period and harvesting date. Knowledge of the genetic control of these traits is necessary to develop cultivars that can face the challenges imposed by global climate change and to target cultivar development as a function of a prolonged vegetation period in the production regions. Different models were tested, including control for effects of population structure and relatedness between cultivars. The full model, controlling for both structure and relatedness, was shown to be the most appropriate to avoid spurious marker-trait associations. When analyzing data over all collections, one significant marker-trait association was obtained for each trait, on chromosomes 9 and 3, for flowering period and harvesting date, respectively. Thereby, genomic locations previously identified in bi-parental populations could now be confirmed for a genetically diverse germplasm.

A critical challenge faced by clinical nephrologists today is the escalating number of patients developing end stage renal disease, a major proportion of which is attributed to diabetic nephropathy (DN). The need for new measures to prevent and treat this disease cannot be overemphasized. To this end, modern genetic approaches provide powerful tools to investigate the etiology of DN. Human studies have already established the importance of genetic susceptibility for DN. Several major susceptibility loci have been identified using linkage studies. In addition, linkage studies in rodents have pinpointed promising chromosomal segments that influence renal traits. Besides augmenting our understanding of disease pathogenesis, these animal studies may facilitate the cloning of disease susceptibility genes in man through the identification of homologous regions that contribute to renal disease. In human diabetes, various genes have been evaluated for their risk contribution to DN. This widespread strategy has been propelled by our knowledge of the glucose-activated pathways underlying DN. Evidence has emerged that a true association does indeed exist for some candidate genes. Furthermore, the in vivo manipulation of gene expression has shown that these genes can modify features of DN in transgenic and knockout rodent models, thus corroborating the findings from human association studies. Still, the exact molecular mechanisms involving these genes remain to be fully elucidated. This formidable task may be accomplished by continuing to harness the synergy between human and experimental genetic approaches. In this respect, our review provides a first synthesis of the current literature to facilitate this challenging effort.